As computer science and technologies continue to evolve, three-dimensional model processing techniques have become one of the essential parts of daily lives, with applications in film producing, medical treatment, industrial manufacturing and various other domains. As one of the fundamental techniques in three-dimensional model interpretation and processing, three-dimensional model co-segmentation plays a key role in three-dimensional modeling, three-dimensional animation, three-dimensional simulation and many other three-dimensional technologies. Three-dimensional model co-segmentation is about jointly segmenting various parts of individual models in a model class including a plurality of models.
In the prior art, three-dimensional model co-segmentation methods include unsupervised three-dimensional model set joint-segmentation method and interactive three-dimensional model set co-segmentation method, etc. In the unsupervised three-dimensional model set joint-segmentation method, each model of an input model set is initially segmented. Then, the pre-segmented parts of different models are jointly segmented in pairs in order to identify similar parts between the models. Finally, all model parts are globally optimized to obtain a consistent co-segmentation outcome for the three-dimensional model set. In the interactive three-dimensional model co-segmentation method, an initial segmentation outcome is produced through a pre-segmentation. Then, a user preemptively imposes constraints on a small number of models regarding the parts which belong to the same or different classes. The constraints are utilized to optimize further co-segmentation outcomes iteratively, until a consistent segmentation outcome of the three-dimensional model set is obtained.
However, in the prior art, the unsupervised three-dimensional model set joint-segmentation method relies on the initial pre-segmentation outcome. If the part segmentation outcome generated by the pre-segmentation process is inappropriate, nor will the final segmentation outcome be. Meanwhile, since the unsupervised three-dimensional model set joint-segmentation method also relies on correlations among the models, in case any erroneous correlation is introduced due to diversity of the models, erroneous segmentation outcome will consequently be produced. In short, the possibility of obtaining a correct co-segmentation outcome is relatively low. The possibility of obtaining a correct co-segmentation outcome through the interactive three-dimensional model set co-segmentation method is, unfortunately, also relatively low. The cause being that, a small amount of user interaction is required to propagate correct correlations among model parts to other models, yet without a true segmentation outcome as the guidance, teachings from the user interaction still have some limitations in terms of correcting erroneous model part correlations, again making correct segmentation outcome unachievable, and the possibility of obtaining a correct co-segmentation outcome is also relatively low.